Identification of a novel transcriptome signature for predicting the response to anti-TNF-α treatment in patients with rheumatoid arthritis.

Objectives: This study aims to identify and validate a transcriptomic signature capable of predicting the response to tumour necrosis factor inhibitors (TNFi) therapy in patients with rheumatoid arthritis (RA) before treatment initiation.

Methods: We performed a retrospective transcriptomic analysis using 2 public datasets, RNA-seq data from peripheral blood mononuclear cells (GSE138746) and microarray data from whole blood (GSE33377), to define a small-scale gene signature predictive of the response to TNFi treatment. Three external validations were then conducted, resulting in a total of 279 individuals, 169 responders, and 110 nonresponders.

Results: Initial RNA-seq analysis (GSE138746) revealed 53 genes differentially expressed between responders and nonresponders; however, none of these genes remained significant after P value adjustment with the Benjamini-Hochberg method. A small-scale genetic signature comprising the 18 most discriminatory genes was then developed, achieving a leave-one-out cross-validation predictive accuracy of 88.75%. We further refined this list to 7 genes (COMTD1, MRPL24, DNTTIP1, GLS2, GTPBP2, IL18R1, and KCNK17) that effectively predicted the response to TNFi treatment, with an area under the receiver operating characteristic curve (AUC) of 0.84 in the GSE33377 dataset. Internal validation of the GSE138746 dataset yielded an AUC = 0.89. Finally, external validation confirmed the robustness of the 7-gene model (AUC ≥ 0.85).

Conclusions: We identified a transcriptomic signature that aids the prediction of the response to TNFi treatment in patients with RA. These findings support its potential use as a precision medicine tool to improve therapeutic decision-making and reduce exposure to ineffective treatments in patients with RA.